Customer data collection systems can perform various inventory control and ordering functions in a variety of environments such as, for example, grocery stores, department stores, and enterprises involving the manufacturing and supply of parts. Tracking the behavior of a repeat customer is of strong interest in a retail application. For example, in a quick service restaurant a customer menu choice can be tracked so that “suggestive selling” can be enabled by offering a product that the customer has ordered in the past based on a history of his or her particular orders.
Several approaches have been proposed to gather customer history data. In one approach, customer history data can be gathered by offering a bonus card and/or a loyalty program (e.g. a shopper's club card). A problem associated with such an approach is that clientele might not wish to enroll in the loyalty program in some retail situations. In another approach, customer information across visits can be tracked by tying an order information with respect to a customer's credit card data. The credit card information can be employed as a unique customer identifier to associate the customer's order information over a time period. Such an approach, however, is not possible for quick service (e.g., McDonald's) type environments, where most customers pay in cash. Another technique involves the use of facial recognition technologies to identify an individual customer across multiple visits and/or store locations. Such facial recognition techniques provide an unconstrained matching problem and due to differences in noise factors such as, for example, lighting and customer attire, such an approach is unable to accurately recognize the facial features.
Based on the foregoing, it is believed that a need exists for an improved system and method for tracking a customer history data, solutions for which will be described in greater detailed herein.